JSON files: open-ended question

Be the JSON file

{
  "students": [
    {
      "id": 19091,
      "firstName": "Peter",
      "lastName": "Mueller",
      "grades": {
          "micro": 5,
          "macro": 4.5,
          "data handling": 5.5
          }
    },
    {
      "id": 19092,
      "firstName": "Anna",
      "lastName": "Schmid",
      "grades": {
          "micro": 5.25,
          "macro": 4,
          "data handling": 5.75
          }
    },
    {
      "id": 19093,
      "firstName": "Noah",
      "lastName": "Trevor",
      "grades": {
          "micro": 4,
          "macro": 4.5,
          "data handling": 5
          }
    }
  ]
}

Write an R code to extract a table with, as a first column, a vector of first names, and as a second column, the average grade per student. The table can be a data frame or a tibble.

XML

<students>
  <student>
    <id>19091</id>
    <firstName>Peter</firstName>
    <lastName>Mueller</lastName>
    <grades>
      <micro>5</micro>
      <macro>4.5</macro>
      <dataHandling>5.5</dataHandling>
    </grades>
  </student>
  <student>
    <id>19092</id>
    <firstName>Anna</firstName>
    <lastName>Schmid</lastName>
    <grades>
      <micro>5.25</micro>
      <macro>4</macro>
      <dataHandling>5.75</dataHandling>
    </grades>
  </student>
  <student>
    <id>19093</id>
    <firstName>Noah</firstName>
    <lastName>Trevor</lastName>
    <grades>
      <micro>4</micro>
      <macro>4.5</macro>
      <dataHandling>5</dataHandling>
    </grades>
  </student>
</students>

• ‘students’ is the root-node, ‘grades’ are its children
• The siblings of Trevor Noah are Anna Schmid and Peter Mueller
• The code below would be an alternative, equivalent notation for the third student in the xml file above.

<student id="19093" firstName="Noah" lastName="Trevor">
      <grades micro="4" macro="4.5" dataHandling="5" />
</student>

The dataset is imported, now what?

• In practice: still a long way to go.
• Parsable, but messy data: inconsistencies, data types, missing observations, wide format.

The dataset is imported, now what?

• In practice: still a long way to go.
• Parsable, but messy data: inconsistencies, data types, missing observations, wide format.

• Goal of data preparation: dataset is ready for analysis.

Part II: Data gathering and preparation

Source: https://www.storybench.org/wp-content/uploads/2017/05/tidyverse.png

Part II: Data preparation, analysis, and visualization

Part II: Data preparation, analysis, and visualization

Data preparation: Beware of the “Garbage in garbage out” problem

Goals for today and next time: master data preparation

Data preparation can be understood as consisting of five main actions:

• Tidy data.
• Reshape datasets from wide to long (and vice versa).
• Bind or stack rows in datasets.
• Join datasets (covered next time).
• Clean data.

Tidy data: some vocabulary

Following R4DS, a tidy dataset is tidy when…

1. Each variable is a column; each column is a variable.
2. Each observation is a row; each row is an observation.
3. Each value is a cell; each cell is a single value.

Tidy data

Tidy data. Source R4DS.

Tidy data is not trivial

In Economics, the definition of an observation can vary:

• Panel data (longitudinal data)
• Cross-sectional data
• Time series

Tidy data is not trivial

Panel Data (Longitudinal Data):

Panel data tracks the same units over time: each unit has multiple observations across time periods.
Observation: a measurement for a specific unit at a particular point in time.

Cross-Sectional Data:

“Snapshot” of different units at the same moment.
Observation: single measurement for each unit at a single point in time.

Time Series Data:

Single unit tracked over time.
Observation: measurement of a single variable for a single unit (or aggregate) over multiple points in time.

Three examples of non-tidy data (1)

Messy 💩

# A tibble: 2 × 4
  measure     `Jan 1` `Jan 2` `Jan 3`
  <chr>         <dbl>   <dbl>   <dbl>
1 Temperature      20      22      21
2 Humidity         80      78      82

Tidy 😎

…

Three examples of non-tidy data (1)

Messy 💩

# A tibble: 2 × 4
  measure     `Jan 1` `Jan 2` `Jan 3`
  <chr>         <dbl>   <dbl>   <dbl>
1 Temperature      20      22      21
2 Humidity         80      78      82

Tidy 😎

# A tibble: 3 × 3
  Date  Temperature Humidity
  <chr>       <dbl>    <dbl>
1 Jan 1          20       80
2 Jan 2          22       78
3 Jan 3          21       82

Three examples of non-tidy data (2)

Messy 💩

# A tibble: 3 × 2
   year temperature_location
  <dbl> <chr>               
1  2019 22C_London          
2  2019 18C_Paris           
3  2019 25C_Rome            

Tidy 😎

homework..

Three examples of non-tidy data (3)

Messy 💩

   Student Econ DataHandling Management
1 Johannes 5.00          4.0        5.5
2   Hannah 5.25          4.5        6.0
3     Igor 4.00          5.0        6.0

Tidy 😎

homework..

Reshaping: the concept

Long and wide data. Source: Hugo Tavares

Reshaping: implementation in R

• From wide to long: melt(), gather(),

👉 We’ll use tidyverse::pivot_longer().

• From long to wide: cast(), spread(),

👉 We’ll use tidyverse::pivot_wider().

Reshaping: implementation in R with tidyverse()

Long and wide data with code. Source: Hugo Tavares

Stack/row-bind: the concept

Stack/row-bind: implementation in R

• Use rbind() in base R
  • Requires that the data frames have the same column names and same column classes.

• Use bind_rows() from dplyr()
  • More flexible
  • Binds data frames with different column names and classes
  • Automatically fills missing columns with NA

For these reasons (+ performance, handling or row names, and handling of factors), dplyr::bind_rows() is preferred in most applications.

Stack/row-bind: code example

# Create three dfs
subset1 <- data.frame(ID = c(1,2),
                      X = c("a", "b"),
                      Y = c(50,10))

subset2 <- data.frame(ID = c(3,4),
                      Z = c("M", "O"))

subset3 <- data.frame(ID = c(5),
                      X = c("c"),
                      Z = "P")

# Inspect
subset1

  ID X  Y
1  1 a 50
2  2 b 10

subset2

  ID Z
1  3 M
2  4 O

subset3

  ID X Z
1  5 c P

Stack/row-bind: code example

# Stack data frames
combined_df_bind_rows <- bind_rows(subset1, subset2, subset3)
combined_df_rbind     <- rbind(subset1, subset2, subset3) 

Error in rbind(deparse.level, ...): numbers of columns of arguments do not match

# What are the following objects?
combined_df_bind_rows

  ID    X  Y    Z
1  1    a 50 <NA>
2  2    b 10 <NA>
3  3 <NA> NA    M
4  4 <NA> NA    O
5  5    c NA    P

combined_df_rbind

Error in eval(expr, envir, enclos): object 'combined_df_rbind' not found